from collections import defaultdict from datetime import datetime from demucs.apply import BagOfModels, apply_model from demucs.hdemucs import HDemucs from demucs.pretrained import get_model as _gm from lib_v5 import dataset from lib_v5 import spec_utils from lib_v5.model_param_init import ModelParameters from models import stft, istft from pathlib import Path from random import randrange from tqdm import tqdm from tkinter import filedialog import lib_v5.filelist import cv2 import hashlib import importlib import librosa import math import numpy as np import os import pydub import shutil import soundfile as sf import time # Timer import tkinter as tk import torch import traceback # Error Message Recent Calls class VocalRemover(object): def __init__(self, data, text_widget: tk.Text): self.data = data self.text_widget = text_widget self.models = defaultdict(lambda: None) self.devices = defaultdict(lambda: None) # self.offset = model.offset data = { 'agg': 10, 'demucsmodel_sel_VR': 'UVR_Demucs_Model_1', 'demucsmodelVR': True, 'export_path': None, 'gpu': -1, 'high_end_process': 'mirroring', 'input_paths': None, 'inst_only': False, 'instrumentalModel': None, 'ModelParams': 'Auto', 'mp3bit': '320k', 'normalize': False, 'output_image': True, 'overlap': 0.5, 'postprocess': True, 'saveFormat': 'wav', 'segment': 'None', 'settest': False, 'shifts': 0, 'split_mode': False, 'tta': True, 'useModel': None, 'voc_only': False, 'wavtype': 'PCM_16', 'window_size': 512, } default_window_size = data['window_size'] default_agg = data['agg'] def update_progress(progress_var, total_files, file_num, step: float = 1): """Calculate the progress for the progress widget in the GUI""" base = (100 / total_files) progress = base * (file_num - 1) progress += base * step progress_var.set(progress) def get_baseText(total_files, file_num): """Create the base text for the command widget""" text = 'File {file_num}/{total_files} '.format(file_num=file_num, total_files=total_files) return text def determineModelFolderName(): """ Determine the name that is used for the folder and appended to the back of the music files """ modelFolderName = '' if not data['modelFolder']: # Model Test Mode not selected return modelFolderName # -Instrumental- if os.path.isfile(data['instrumentalModel']): modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0] if modelFolderName: modelFolderName = '/' + modelFolderName return modelFolderName def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress_var: tk.Variable, **kwargs: dict): global model_params_d global nn_arch_sizes global nn_architecture global overlap_set global shift_set global split_mode global demucs_model_set global wav_type_set global flac_type_set global mp3_bit_set wav_type_set = data['wavtype'] #Error Handling runtimeerr = "CUDNN error executing cudnnSetTensorNdDescriptor" systemmemerr = "DefaultCPUAllocator: not enough memory" cuda_err = "CUDA out of memory" mod_err = "ModuleNotFoundError" file_err = "FileNotFoundError" ffmp_err = """audioread\__init__.py", line 116, in audio_open""" sf_write_err = "sf.write" try: with open('errorlog.txt', 'w') as f: f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: VR Architecture' + f'\nLast Conversion Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass nn_arch_sizes = [ 31191, # default 33966, 123821, 123812, 129605, 537238 # custom ] nn_architecture = list('{}KB'.format(s) for s in nn_arch_sizes) def save_files(wav_instrument, wav_vocals): """Save output music files""" vocal_name = '(Vocals)' instrumental_name = '(Instrumental)' save_path = os.path.dirname(base_name) # Swap names if vocal model VModel="Vocal" if VModel in model_name: # Reverse names vocal_name, instrumental_name = instrumental_name, vocal_name # Save Temp File # For instrumental the instrumental is the temp file # and for vocal the instrumental is the temp file due # to reversement if data['demucsmodelVR']: samplerate = 44100 else: samplerate = mp.param['sr'] sf.write(f'temp.wav', normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set) appendModelFolderName = modelFolderName.replace('/', '_') # -Save files- # Instrumental if instrumental_name is not None: if data['modelFolder']: instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',) instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',) instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{instrumental_name}',) else: instrumental_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{instrumental_name}',) instrumental_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{instrumental_name}',) instrumental_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{instrumental_name}',) if os.path.isfile(instrumental_path): file_exists_i = 'there' else: file_exists_i = 'not_there' if VModel in model_name and data['voc_only']: sf.write(instrumental_path, normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set) elif VModel in model_name and data['inst_only']: pass elif data['voc_only']: pass else: sf.write(instrumental_path, normalization_set(wav_instrument).T, samplerate, subtype=wav_type_set) # Vocal if vocal_name is not None: if data['modelFolder']: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',) vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',) vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}{appendModelFolderName}_{vocal_name}',) else: vocal_path = '{save_path}/{file_name}.wav'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{vocal_name}',) vocal_path_mp3 = '{save_path}/{file_name}.mp3'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{vocal_name}',) vocal_path_flac = '{save_path}/{file_name}.flac'.format( save_path=save_path, file_name=f'{os.path.basename(base_name)}_{vocal_name}',) if os.path.isfile(vocal_path): file_exists_v = 'there' else: file_exists_v = 'not_there' if VModel in model_name and data['inst_only']: sf.write(vocal_path, normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set) elif VModel in model_name and data['voc_only']: pass elif data['inst_only']: pass else: sf.write(vocal_path, normalization_set(wav_vocals).T, samplerate, subtype=wav_type_set) if data['saveFormat'] == 'Mp3': try: if data['inst_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(vocal_path) musfile.export(vocal_path_mp3, format="mp3", bitrate=mp3_bit_set) if file_exists_v == 'there': pass else: try: os.remove(vocal_path) except: pass if data['voc_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(instrumental_path) musfile.export(instrumental_path_mp3, format="mp3", bitrate=mp3_bit_set) if file_exists_i == 'there': pass else: try: os.remove(instrumental_path) except: pass except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write(base_text + 'Failed to save output(s) as Mp3(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as mp3 "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass if data['saveFormat'] == 'Flac': try: if VModel in model_name: if data['inst_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(instrumental_path) musfile.export(instrumental_path_flac, format="flac") if file_exists_v == 'there': pass else: try: os.remove(instrumental_path) except: pass if data['voc_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(vocal_path) musfile.export(vocal_path_flac, format="flac") if file_exists_i == 'there': pass else: try: os.remove(vocal_path) except: pass else: if data['inst_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(vocal_path) musfile.export(vocal_path_flac, format="flac") if file_exists_v == 'there': pass else: try: os.remove(vocal_path) except: pass if data['voc_only'] == True: pass else: musfile = pydub.AudioSegment.from_wav(instrumental_path) musfile.export(instrumental_path_flac, format="flac") if file_exists_i == 'there': pass else: try: os.remove(instrumental_path) except: pass except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if "ffmpeg" in errmessage: text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'FFmpeg might be missing or corrupted, please check error log.\n') text_widget.write(base_text + 'Moving on...\n') else: text_widget.write(base_text + 'Failed to save output(s) as Flac(s).\n') text_widget.write(base_text + 'Please check error log.\n') text_widget.write(base_text + 'Moving on...\n') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to save file as flac "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'FFmpeg might be missing or corrupted.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass data.update(kwargs) # Update default settings global default_window_size global default_agg global normalization_set default_window_size = data['window_size'] default_agg = data['agg'] stime = time.perf_counter() progress_var.set(0) text_widget.clear() button_widget.configure(state=tk.DISABLED) # Disable Button overlap_set = float(data['overlap']) shift_set = int(data['shifts']) demucs_model_set = data['demucsmodel_sel_VR'] split_mode = data['split_mode'] if data['wavtype'] == '32-bit Float': wav_type_set = 'FLOAT' elif data['wavtype'] == '64-bit Float': wav_type_set = 'DOUBLE' else: wav_type_set = data['wavtype'] flac_type_set = data['flactype'] mp3_bit_set = data['mp3bit'] if data['normalize'] == True: normalization_set = spec_utils.normalize print('normalization on') else: normalization_set = spec_utils.nonormalize print('normalization off') vocal_remover = VocalRemover(data, text_widget) modelFolderName = determineModelFolderName() timestampnum = round(datetime.utcnow().timestamp()) randomnum = randrange(100000, 1000000) # Separation Preperation try: #Load File(s) for file_num, music_file in enumerate(data['input_paths'], start=1): # Determine File Name m=music_file if data['settest']: try: base_name = f'{data["export_path"]}/{str(timestampnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' except: base_name = f'{data["export_path"]}/{str(randomnum)}_{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' else: base_name = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}' model_name = os.path.basename(data[f'{data["useModel"]}Model']) model = vocal_remover.models[data['useModel']] device = vocal_remover.devices[data['useModel']] # -Get text and update progress- base_text = get_baseText(total_files=len(data['input_paths']), file_num=file_num) progress_kwargs = {'progress_var': progress_var, 'total_files': len(data['input_paths']), 'file_num': file_num} update_progress(**progress_kwargs, step=0) try: total, used, free = shutil.disk_usage("/") total_space = int(total/1.074e+9) used_space = int(used/1.074e+9) free_space = int(free/1.074e+9) if int(free/1.074e+9) <= int(2): text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if int(free/1.074e+9) in [3, 4, 5, 6, 7, 8]: text_widget.write('Warning: Your main drive is running low on storage. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly.\n\n') text_widget.write('Detected Total Space: ' + str(total_space) + ' GB' + '\n') text_widget.write('Detected Used Space: ' + str(used_space) + ' GB' + '\n') text_widget.write('Detected Free Space: ' + str(free_space) + ' GB' + '\n\n') except: pass if data['wavtype'] == '64-bit Float': if data['saveFormat'] == 'Flac': text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if data['wavtype'] == '64-bit Float': if data['saveFormat'] == 'Mp3': text_widget.write('Please select \"WAV\" as your save format to use 64-bit Float.\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return #Load Model text_widget.write(base_text + 'Loading model...') model_size = math.ceil(os.stat(data['instrumentalModel']).st_size / 1024) nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size))) nets = importlib.import_module('lib_v5.nets' + f'_{nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None) aggresive_set = float(data['agg']/100) ModelName=(data['instrumentalModel']) #Package Models text_widget.write('Done!\n') if data['ModelParams'] == 'Auto': model_hash = hashlib.md5(open(ModelName,'rb').read()).hexdigest() model_params = [] model_params = lib_v5.filelist.provide_model_param_hash(model_hash) print(model_params) if model_params[0] == 'Not Found Using Hash': model_params = [] model_params = lib_v5.filelist.provide_model_param_name(ModelName) if model_params[0] == 'Not Found Using Name': text_widget.write(base_text + f'Unable to set model parameters automatically with the selected model.\n') confirm = tk.messagebox.askyesno(title='Unrecognized Model Detected', message=f'\nThe application could not automatically set the model param for the selected model.\n\n' + f'Would you like to select the model param file for this model?\n\n') if confirm: model_param_selection = filedialog.askopenfilename(initialdir='lib_v5/modelparams', title=f'Select Model Param', filetypes=[("Model Param", "*.json")]) model_param_file_path = str(model_param_selection) model_param_file = os.path.splitext(os.path.basename(model_param_file_path))[0] + '.json' model_params = [model_param_file_path, model_param_file] with open(f"lib_v5/filelists/model_cache/vr_param_cache/{model_hash}.txt", 'w') as f: f.write(model_param_file) if model_params[0] == '': text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Model parameters are missing.\n\n') text_widget.write(f'Please check the following:\n') text_widget.write(f'1. Make sure the model is still present.\n') text_widget.write(f'2. If you are running a model that was not originally included in this package, \nplease append the modelparam name to the model name.\n') text_widget.write(f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n') text_widget.write(f'Please address this and try again.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: pass else: text_widget.write(base_text + f'Model param not selected.\n') text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Model parameters are missing.\n\n') text_widget.write(f'Please check the following:\n') text_widget.write(f'1. Make sure the model is still present.\n') text_widget.write(f'2. If you are running a model that was not originally included in this package, \nplease append the modelparam name to the model name.\n') text_widget.write(f' - Example if using \"4band_v2.json\" modelparam: \"model_4band_v2.pth\"\n\n') text_widget.write(f'Please address this and try again.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return else: param = data['ModelParams'] model_param_file_path = f'lib_v5/modelparams/{param}' model_params = [model_param_file_path, param] text_widget.write(base_text + 'Loading assigned model parameters ' + '\"' + model_params[1] + '\"... ') mp = ModelParameters(model_params[0]) text_widget.write('Done!\n') # -Instrumental- if os.path.isfile(data['instrumentalModel']): device = torch.device('cpu') model = nets.CascadedASPPNet(mp.param['bins'] * 2) model.load_state_dict(torch.load(data['instrumentalModel'], map_location=device)) if torch.cuda.is_available() and data['gpu'] >= 0: device = torch.device('cuda:{}'.format(data['gpu'])) model.to(device) vocal_remover.models['instrumental'] = model vocal_remover.devices['instrumental'] = device model_name = os.path.basename(data[f'{data["useModel"]}Model']) # -Go through the different steps of Separation- # Wave source text_widget.write(base_text + 'Loading audio source...') X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} bands_n = len(mp.param['band']) for d in range(bands_n, 0, -1): bp = mp.param['band'][d] if d == bands_n: # high-end band X_wave[d], _ = librosa.load( music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) if X_wave[d].ndim == 1: X_wave[d] = np.asarray([X_wave[d], X_wave[d]]) else: # lower bands X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) # Stft of wave source X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']) if d == bands_n and data['high_end_process'] != 'none': input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start']) input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :] text_widget.write('Done!\n') update_progress(**progress_kwargs, step=0.1) text_widget.write(base_text + 'Loading the stft of audio source...') text_widget.write(' Done!\n') text_widget.write(base_text + "Please Wait...\n") X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp) del X_wave, X_spec_s def inference(X_spec, device, model, aggressiveness): def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness): model.eval() with torch.no_grad(): preds = [] iterations = [n_window] total_iterations = sum(iterations) text_widget.write(base_text + "Processing "f"{total_iterations} Slices... ") for i in tqdm(range(n_window)): update_progress(**progress_kwargs, step=(0.1 + (0.8/n_window * i))) start = i * roi_size X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']] X_mag_window = torch.from_numpy(X_mag_window).to(device) pred = model.predict(X_mag_window, aggressiveness) pred = pred.detach().cpu().numpy() preds.append(pred[0]) pred = np.concatenate(preds, axis=2) text_widget.write('Done!\n') return pred def preprocess(X_spec): X_mag = np.abs(X_spec) X_phase = np.angle(X_spec) return X_mag, X_phase X_mag, X_phase = preprocess(X_spec) coef = X_mag.max() X_mag_pre = X_mag / coef n_frame = X_mag_pre.shape[2] pad_l, pad_r, roi_size = dataset.make_padding(n_frame, data['window_size'], model.offset) n_window = int(np.ceil(n_frame / roi_size)) X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred = pred[:, :, :n_frame] if data['tta']: pad_l += roi_size // 2 pad_r += roi_size // 2 n_window += 1 X_mag_pad = np.pad( X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant') pred_tta = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness) pred_tta = pred_tta[:, :, roi_size // 2:] pred_tta = pred_tta[:, :, :n_frame] return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase) else: return pred * coef, X_mag, np.exp(1.j * X_phase) aggressiveness = {'value': aggresive_set, 'split_bin': mp.param['band'][1]['crop_stop']} if data['tta']: text_widget.write(base_text + "Running Inferences (TTA)...\n") else: text_widget.write(base_text + "Running Inference...\n") pred, X_mag, X_phase = inference(X_spec_m, device, model, aggressiveness) update_progress(**progress_kwargs, step=0.9) # Postprocess if data['postprocess']: try: text_widget.write(base_text + 'Post processing...') pred_inv = np.clip(X_mag - pred, 0, np.inf) pred = spec_utils.mask_silence(pred, pred_inv) text_widget.write(' Done!\n') except Exception as e: text_widget.write('\n' + base_text + 'Post process failed, check error log.\n') text_widget.write(base_text + 'Moving on...\n') traceback_text = ''.join(traceback.format_tb(e.__traceback__)) errmessage = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while attempting to run Post Processing on "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass update_progress(**progress_kwargs, step=0.95) # Inverse stft y_spec_m = pred * X_phase v_spec_m = X_spec_m - y_spec_m def demix_demucs(mix): #print('shift_set ', shift_set) text_widget.write(base_text + "Running Demucs Inference...\n") text_widget.write(base_text + "Processing... ") print(' Running Demucs Inference...') mix = torch.tensor(mix, dtype=torch.float32) ref = mix.mean(0) mix = (mix - ref.mean()) / ref.std() with torch.no_grad(): sources = apply_model(demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0] text_widget.write('Done!\n') sources = (sources * ref.std() + ref.mean()).cpu().numpy() sources[[0,1]] = sources[[1,0]] return sources def demucs_prediction(m): global demucs_sources mix, samplerate = librosa.load(m, mono=False, sr=44100) if mix.ndim == 1: mix = np.asfortranarray([mix,mix]) mix = mix.T demucs_sources = demix_demucs(mix.T) if data['demucsmodelVR']: demucs = HDemucs(sources=["other", "vocals"]) text_widget.write(base_text + 'Loading Demucs model... ') update_progress(**progress_kwargs, step=0.95) path_d = Path('models/Demucs_Models/v3_repo') #print('What Demucs model was chosen? ', demucs_model_set) demucs = _gm(name=demucs_model_set, repo=path_d) text_widget.write('Done!\n') #print('segment: ', data['segment']) if data['segment'] == 'None': segment = None if isinstance(demucs, BagOfModels): if segment is not None: for sub in demucs.models: sub.segment = segment else: if segment is not None: sub.segment = segment else: try: segment = int(data['segment']) if isinstance(demucs, BagOfModels): if segment is not None: for sub in demucs.models: sub.segment = segment else: if segment is not None: sub.segment = segment text_widget.write(base_text + "Segments set to "f"{segment}.\n") except: segment = None if isinstance(demucs, BagOfModels): if segment is not None: for sub in demucs.models: sub.segment = segment else: if segment is not None: sub.segment = segment #print('segment port-process: ', segment) demucs.cpu() demucs.eval() demucs_prediction(m) if data['voc_only'] and not data['inst_only']: pass else: text_widget.write(base_text + 'Saving Instrumental... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], y_spec_m, input_high_end, mp) if data['demucsmodelVR']: wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=True) demucs_inst = demucs_sources[0] sources = [wav_instrument,demucs_inst] spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)] ln = min([spec[0].shape[2], spec[1].shape[2]]) spec[0] = spec[0][:,:,:ln] spec[1] = spec[1][:,:,:ln] v_spec_c = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0]) wav_instrument = istft(v_spec_c,1024) else: wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp, input_high_end_h, input_high_end_, demucs=False) if data['voc_only'] and not data['inst_only']: pass else: text_widget.write('Done!\n') else: wav_instrument = spec_utils.cmb_spectrogram_to_wave_d(y_spec_m, mp) if data['voc_only'] and not data['inst_only']: pass else: text_widget.write('Done!\n') if data['inst_only'] and not data['voc_only']: pass else: text_widget.write(base_text + 'Saving Vocals... ') if data['high_end_process'].startswith('mirroring'): input_high_end_ = spec_utils.mirroring(data['high_end_process'], v_spec_m, input_high_end, mp) if data['demucsmodelVR']: wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=True) demucs_voc = demucs_sources[1] sources = [wav_vocals,demucs_voc] spec = [stft(sources[0],2048,1024),stft(sources[1],2048,1024)] ln = min([spec[0].shape[2], spec[1].shape[2]]) spec[0] = spec[0][:,:,:ln] spec[1] = spec[1][:,:,:ln] v_spec_c = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0]) wav_vocals = istft(v_spec_c,1024) else: wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, input_high_end_h, input_high_end_, demucs=False) if data['inst_only'] and not data['voc_only']: pass else: text_widget.write('Done!\n') else: wav_vocals = spec_utils.cmb_spectrogram_to_wave_d(v_spec_m, mp, demucs=False) if data['inst_only'] and not data['voc_only']: pass else: text_widget.write('Done!\n') update_progress(**progress_kwargs, step=1) # Save output music files save_files(wav_instrument, wav_vocals) update_progress(**progress_kwargs, step=1) # Save output image if data['output_image']: with open('{}_Instruments.jpg'.format(base_name), mode='wb') as f: image = spec_utils.spectrogram_to_image(y_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) with open('{}_Vocals.jpg'.format(base_name), mode='wb') as f: image = spec_utils.spectrogram_to_image(v_spec_m) _, bin_image = cv2.imencode('.jpg', image) bin_image.tofile(f) text_widget.write(base_text + 'Completed Separation!\n\n') except Exception as e: traceback_text = ''.join(traceback.format_tb(e.__traceback__)) message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\n' if runtimeerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'Your PC cannot process this audio file with the chunk size selected.\nPlease lower the chunk size and try again.\n\n' + f'If this error persists, please contact the developers.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if cuda_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough GPU memory to use this model.\n') text_widget.write(f'Please close any GPU intensive applications and try again.\n') text_widget.write(f'If the error persists, your GPU might not be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'The application was unable to allocate enough GPU memory to use this model.\n' + f'Please close any GPU intensive applications and try again.\n' + f'If the error persists, your GPU might not be supported.\n\n' + f'Raw error details:\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if mod_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Application files(s) are missing.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write(f'Please check for missing files/scripts in the app directory and try again.\n') text_widget.write(f'If the error persists, please reinstall application or contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'Application files(s) are missing.\n' + f'Please check for missing files/scripts in the app directory and try again.\n' + f'If the error persists, please reinstall application or contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if file_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Missing file error raised.\n') text_widget.write("\n" + f'{type(e).__name__} - "{e}"' + "\n\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'Missing file error raised.\n' + "\n" + f'Please address the error and try again.' + "\n" + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if ffmp_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The input file type is not supported or FFmpeg is missing.\n') text_widget.write(f'Please select a file type supported by FFmpeg and try again.\n\n') text_widget.write(f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files \nuntil it is available on this system.\n\n') text_widget.write(f'See the \"More Info\" tab in the Help Guide.\n\n') text_widget.write(f'If this error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'The input file type is not supported or FFmpeg is missing.\nPlease select a file type supported by FFmpeg and try again.\n\n' + f'If FFmpeg is missing or not installed, you will only be able to process \".wav\" files until it is available on this system.\n\n' + f'See the \"More Info\" tab in the Help Guide.\n\n' + f'If this error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if sf_write_err in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'Could not write audio file.\n') text_widget.write(f'This could be due to low storage on target device or a system permissions issue.\n') text_widget.write(f"\nFor raw error details, go to the Error Log tab in the Help Guide.\n") text_widget.write(f'\nIf the error persists, please contact the developers.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'Could not write audio file.\n' + f'This could be due to low storage on target device or a system permissions issue.\n' + f'If the error persists, please contact the developers.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return if systemmemerr in message: text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n\n') text_widget.write(f'The application was unable to allocate enough system memory to use this \nmodel.\n\n') text_widget.write(f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n') text_widget.write(f'Please Note: Intel Pentium and Intel Celeron processors do not work well with \nthis application.\n\n') text_widget.write(f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'The application was unable to allocate enough system memory to use this model.\n' + f'Please do the following:\n\n1. Restart this application.\n2. Ensure any CPU intensive applications are closed.\n3. Then try again.\n\n' + f'Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.\n\n' + f'If the error persists, the system may not have enough RAM, or your CPU might \nnot be supported.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: pass torch.cuda.empty_cache() progress_var.set(0) button_widget.configure(state=tk.NORMAL) # Enable Button return print(traceback_text) print(type(e).__name__, e) print(message) try: with open('errorlog.txt', 'w') as f: f.write(f'Last Error Received:\n\n' + f'Error Received while processing "{os.path.basename(music_file)}":\n' + f'Process Method: VR Architecture\n\n' + f'If this error persists, please contact the developers with the error details.\n\n' + message + f'\nError Time Stamp [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n') except: tk.messagebox.showerror(master=window, title='Error Details', message=message) progress_var.set(0) text_widget.write("\n" + base_text + f'Separation failed for the following audio file:\n') text_widget.write(base_text + f'"{os.path.basename(music_file)}"\n') text_widget.write(f'\nError Received:\n') text_widget.write("\nFor raw error details, go to the Error Log tab in the Help Guide.\n") text_widget.write("\n" + f'Please address the error and try again.' + "\n") text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) # Enable Button return try: os.remove('temp.wav') except: pass progress_var.set(0) text_widget.write(f'Conversion(s) Completed!\n') text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}') # nopep8 torch.cuda.empty_cache() button_widget.configure(state=tk.NORMAL) # Enable Button